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Personalization of Supermarket Product Recommendations IBM Research Report (2000) R.D. Lawrence et al. Julian Keenaghan 1 Introduction Personalized recommender system designed to suggest new products to supermarket shoppers Based upon their previous purchase behaviour and expected product appeal Shoppers use PDA’s Alternative source of new ideas Julian Keenaghan 2 Introduction continued Content-based filtering based on what person has liked in the past measure of distance between vectors representing: Personal preferences Products overspecialization Collaborative filtering items that similar people have liked Associations mining (product domain) Clustering (customer domain) Julian Keenaghan 3 Product Taxonomy Classes (99) Subclasses (2302) Soft Drinks Dried Cat Food ….. Dried Dog Food Petfoods Julian Keenaghan Fresh Beef Beef Joints Canned Cat Food Products (~30000) ….. Friskies Liver (250g) 4 Overview Normalized customer vectors Customer Purchase Database Data Mining Clustering Product Database Cluster assignments Products eligible for recommendation Cluster-specific Product lists Product list for target customer’s Data Mining Associations cluster Product affinities Matching Algorithm Julian Keenaghan Personalized Recommendation List 5 Customer Model Customer profile C(m)s, for each customer At subclass level => 2303 dim space Normalized fractional spending Vector, quantifies customer’s interest in subclass relative to entire customer database value of 1 implies average level of interest in a subclass Julian Keenaghan 6 Clustering Analysis To identify groups of shoppers with similar spending histories Cluster-specific list of popular products used as input to recommender Clustered at 99-dim product-class level Neural, demographic clustering algorithms Clusters evaluated in terms of dominant attributes: products which most distinguish members of the cluster Cluster 1 – Wines/Beers/Spirits Cluster 2 – Frozen foods Cluster 3 - Baby products, household items etc.. Julian Keenaghan 7 Associations Mining Determine relationships among product classes or subclasses Used IBM’s “Intelligent Miner for Data” Apriori algorithm Support, Confidence, Lift factors Rule: Fresh Beef => Pork/Lamb Support Confidence Lift 0.016 0.33 4.9 Rule: Baby:Disposable Nappies => Baby:Wipes Julian Keenaghan 8 Product Model Each product, n, represented by a 2303-dim vector P(n) Individual entries Ps(n) reflect the “affinity” the product has to subclass s. Ps(n) = 1.0 if s = S(n) (same subclass) 1.0 if S(n) s (associated subclass) 0.5 if C(s) = C(n) (same class) 0.25 if C(n) C(s) 0 (associated class) otherwise Julian Keenaghan 9 Matching Algorithm Score each product for a specific customer and select the best matches. Cosine coefficient metric used C is the customer vector P is the product vector σ mn is the score between customer m and product n σmn = ρn C(m). P(n) / ||C(m)|| ||P(n)|| Julian Keenaghan 10 Matching Algorithm ctd. Limit recommendations for each customer to 1 per product subclass, and 2 per class. 10 to 20 products returned to PDA Previously bought products excluded Data from 20,000 customers Recommendations for 200 Julian Keenaghan 11 Results Recommendations generated weekly 8 months, 200 customers from one store “Respectable” 1.8% boost in revenue from purchases from the list of recommended products. Accepted Recommendations from product classes new to the customer Certain products more amenable to recommendations. Wine vs. household care. “interesting” recommendations Julian Keenaghan 12 Summary Product recommendation system for grocery shopping Content and Collaborative filtering Purchasing history Associations Mining Clustering Revenue boosts ~2% Julian Keenaghan 13